Typically such composite panels are constructed by stacking plies with different orientations. At any point in the panel a “ply percentage” can be defined, indicating the percentage of plies with a given orientation (or equivalently, volume fractions can be defined indicating the volume of plies with a given orientation). It is desirable to design such a composite panel with variable laminate ply percentages across the panel. However, starting with a laminate thickness/percentage formulation it is difficult to design a set of stacking sequences and ply layouts that fulfil both global ply continuity requirements and local stacking sequence design rules.
Typically the transformation from a laminate thickness/percentage formulation to a stacking sequence formulation has been performed using stacking sequence tables. A stacking sequence table describes a unique stacking sequence for each discrete laminate thickness. The laminate stacking sequence table is designed to satisfy both global ply continuity rules for increasing/decreasing laminate thickness and also local stacking sequence design rules. Typically the stacking sequence table is also constructed to have constant laminate ply percentages for all thickness values.
In optimisation runs with constant laminate ply percentages, the use of a laminate stacking sequence table makes it very easy to transform a percentage solution into a stacking sequence solution.
A simplified method of designing the panel is to initially work with thicknesses and laminate percentages and then later convert them into stacking sequences. This allows optimisation by numerical methods. However, for optimisation of a design with variable laminate percentages across the panel such a stacking sequence approach is not sufficient. An efficient method is therefore required to convert a laminate percentage solution into a stacking sequence solution.
Genetic algorithms (GA) for laminate stacking sequence optimisation would initially seem to offer a solution to the stacking sequence identification problem. However consider that it is necessary to identify individual stacking sequences for panels (such as aircraft wing covers) with say 500 individual zones. For an optimised design each zone could have a different thickness and different laminate percentage. A conventional genetic algorithm approach to stacking sequence optimisation would optimise the stacking sequence in each zone and try to satisfy both inter-zone ply continuity requirements and local stacking sequence rules.
Assume for a moment that each zone has 10 plies. The total number of possible stacking sequence permutations considering just a single zone equals 10!=3,628,800. Next consider the problem of designing just two neighbouring zones. The number of design permutations considering individual stacking sequences in the two neighbouring zones is now (10!)^2=13,168,189,440,000. Now, imagine expanding this to consider all possible design permutations for 500 zones, so (10!)^500.
A genetic algorithm works by considering a population of discrete design configuration and refines this population by a systematic search using ideas from evolution theory. For the above problem clearly a genetic algorithm will only ever be able to cover a fraction of the total design space. A straightforward genetic algorithm approach with inter-zone constraints is therefore not thought to be a feasible option.